Robots are becoming more capable of performing mundane chores such as cleaning a room, taking out the trash, and other chores that require robots to interact with everyday objects. While robots are adept at identifying and/or locating objects that are directly in view of one or more vision sensors integrated with the robots or elsewhere, they are less efficient in locating and identifying objects that are not directly in view. Without being provided with preexisting knowledge about where objects of interest (e.g., object to be acted upon by robots) are located, conventional robots may be required to perform time-consuming operations, such as simultaneous localization and mapping (“SLAM”), to exhaustively map an environment and empirically gain knowledge regarding locations of specific objects of interest. These operations may expend resources such as power, processing cycles, memory, and/or time that might be better used for other purposes or at least conserved. Moreover, in an environment such as a home or business, robots performing SLAM and other similar knowledge-gaining operations may be disruptive.
Humans tend to place particular types of objects in predictable locations. For example, trash bins are often positioned beneath or to the side of a desk or table. Dishware and other kitchen objects are often positioned on horizontal surfaces such as table tops and/or counter tops. When a human wishes to interact with an object, the human does not need to exhaustively scan an environment to locate/identify the object. Instead, the human uses a posteriori knowledge gained over a lifetime to narrow the search space to locations at which the human knows the object of interest is likely to be. In this way, humans remain far more efficient than conventional robots in finding objects of interest.